The insight and experience gained by a researcher are often lost because the current productive and analytics software are inherently data-centric, disconnected, and scattered.
Generally, large-scale research efforts, such as drug discovery, are complicated, capital-intensive, and lengthy processes that often involve expertise from multiple scientific disciplines. Because such efforts are multidisciplinary in nature, data sets generated and used by researchers are often as diverse as the fields they are in. In addition, advances in instrumentation and information technology have led to an unprecedented availability of scientific data and metadata that are complex and highly interconnected.
Common characteristics found among research tools are that they are inherently data-centric, disconnected, and scattered. For example, performing a routine medicinal chemistry task of performing similarity and substructure searches, followed by looking for pharmacology and protein structure data, often requires juggling multiple tools and resources. Saving the search results is tedious, and often the entire process needs to be repeated multiple times using different starting or substructures to explore design hypotheses. Remarkably, despite the obvious inconvenience frequently experienced by researchers, there are no tools to address this balancing act of exploring hypotheses and keeping track of what has been done. The insight and experience gained by researchers are often found in their activity history, which is valuable information not utilized fully by the current productive and analytics software.
The above information disclosed in this Background section is only for enhancement of understanding of the background of the invention, and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art.